{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas  as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "import random\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "                  时间     直播标题   主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n0    2023/10/1 13:00   美妆技巧分享     小美  1500  120  30.0   10   200    50   100   \n1    2023/10/2 15:30    健身训练课   健康达人  2000  150  40.0   15   300    70    80   \n2    2023/10/3 20:15    美食烹饪秀   厨艺大师  1800  130  35.0   12   250    60    90   \n3    2023/10/4 14:45   音乐直播演奏  音乐家小明  1200   90   NaN   10   150    30    50   \n4    2023/10/5 19:00   时尚搭配分享   时尚达人  1700  110  28.0   10   180    40    70   \n5    2023/10/6 16:30     萌宠乐园   小宠物家   900   80   NaN    5   100    20    30   \n6    2023/10/7 21:00   旅游美景展示    NaN  2200  180  45.0   18   350    80   110   \n7    2023/10/8 11:45   健康饮食分享    营养师  1400  100  30.0   10   180    40    60   \n8    2023/10/9 18:15   摄影技巧教学  摄影师小李  1600  110  32.0    8   220    50    75   \n9   2023/10/10 14:30     游戏直播   游戏达人  2500  200   NaN   20   400   100   120   \n10  2023/10/11 19:30   艺术绘画展示   画家大师  1100   70  18.0    6   120    25    40   \n11  2023/10/12 17:00   教育知识分享  知识小达人  1300   95  28.0   10   150    30    55   \n12  2023/10/13 22:15  手工DIY课程    NaN  1000   75  20.0    5   130    25    45   \n13  2023/10/14 12:45   科技科普讲座  科技小天才   800   60  15.0    4   100    20    35   \n14  2023/10/15 20:30   情感心理分享   心理医生  1200   90  25.0   10   160    35    60   \n\n    直播时长（分钟）  观看时长（小时）  \n0         90       750  \n1         60       800  \n2        120       950  \n3         75       600  \n4        100       700  \n5         45       450  \n6        150      1000  \n7         80       550  \n8        110       720  \n9        180      1050  \n10        60       580  \n11        70       650  \n12        50       500  \n13        40       420  \n14        75       720  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023/10/4 14:45</td>\n      <td>音乐直播演奏</td>\n      <td>音乐家小明</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>NaN</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>75</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023/10/5 19:00</td>\n      <td>时尚搭配分享</td>\n      <td>时尚达人</td>\n      <td>1700</td>\n      <td>110</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>100</td>\n      <td>700</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023/10/6 16:30</td>\n      <td>萌宠乐园</td>\n      <td>小宠物家</td>\n      <td>900</td>\n      <td>80</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>45</td>\n      <td>450</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023/10/7 21:00</td>\n      <td>旅游美景展示</td>\n      <td>NaN</td>\n      <td>2200</td>\n      <td>180</td>\n      <td>45.0</td>\n      <td>18</td>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n      <td>150</td>\n      <td>1000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023/10/8 11:45</td>\n      <td>健康饮食分享</td>\n      <td>营养师</td>\n      <td>1400</td>\n      <td>100</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>80</td>\n      <td>550</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023/10/9 18:15</td>\n      <td>摄影技巧教学</td>\n      <td>摄影师小李</td>\n      <td>1600</td>\n      <td>110</td>\n      <td>32.0</td>\n      <td>8</td>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>110</td>\n      <td>720</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023/10/10 14:30</td>\n      <td>游戏直播</td>\n      <td>游戏达人</td>\n      <td>2500</td>\n      <td>200</td>\n      <td>NaN</td>\n      <td>20</td>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>180</td>\n      <td>1050</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023/10/11 19:30</td>\n      <td>艺术绘画展示</td>\n      <td>画家大师</td>\n      <td>1100</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>6</td>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>60</td>\n      <td>580</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023/10/12 17:00</td>\n      <td>教育知识分享</td>\n      <td>知识小达人</td>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>70</td>\n      <td>650</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023/10/13 22:15</td>\n      <td>手工DIY课程</td>\n      <td>NaN</td>\n      <td>1000</td>\n      <td>75</td>\n      <td>20.0</td>\n      <td>5</td>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n      <td>50</td>\n      <td>500</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023/10/14 12:45</td>\n      <td>科技科普讲座</td>\n      <td>科技小天才</td>\n      <td>800</td>\n      <td>60</td>\n      <td>15.0</td>\n      <td>4</td>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>40</td>\n      <td>420</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023/10/15 20:30</td>\n      <td>情感心理分享</td>\n      <td>心理医生</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>25.0</td>\n      <td>10</td>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>75</td>\n      <td>720</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Python数据分析\\\\月考题\\\\大数据 大数据系 专高5 《Python数据分析EDA》月考题库（修改2）\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\3-2 大数据 大数据系 专高5 《Python数据分析EDA》月考题库（新建）-已入库\\\\02-02-5-00003\\\\02-02-5-00003\\\\直播数据集.csv\",encoding=\"GBK\")\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [],
   "source": [
    "class LiveShow():\n",
    "\tdef __init__(self,bt=None,xm=None,rs=None,dz=None,pl=None,fx=None):\n",
    "\t\tself.bt = bt\n",
    "\t\tself.xm = xm\n",
    "\t\tself.rs = rs\n",
    "\t\tself.dz = dz\n",
    "\t\tself.pl = pl\n",
    "\t\tself.fx = fx\n",
    "\n",
    "\tdef get_rate(self,l):\n",
    "\t\tall_dz=  sum([i.dz for i in l])\n",
    "\t\tall_rs=  sum([i.rs for i in l])\n",
    "\t\treturn all_dz/all_rs\n",
    "\n",
    "\tdef cal_person_count(self,l):\n",
    "\t\tmax = 0\n",
    "\t\tobj = None\n",
    "\t\tfor i in l:\n",
    "\t\t\tif i.rs > max:\n",
    "\t\t\t\tmax = i.rs\n",
    "\t\t\t\tobj = i\n",
    "\t\treturn obj\n",
    "\n",
    "\tdef stat_biz_type(self,l):\n",
    "\t\ts =set({})\n",
    "\t\tfor i in l:\n",
    "\t\t\ts.add(i.fx)\n",
    "\t\treturn s\n",
    "\n",
    "\tdef get_hot(self,l):\n",
    "\t\thot_list = []\n",
    "\t\tfor i in l:\n",
    "\t\t\tif i.rs > 1500:\n",
    "\t\t\t\thot_list.append(i)\n",
    "\t\treturn hot_list\n",
    "\n",
    "\tdef save(self,l):\n",
    "\t\tfor i in l:\n",
    "\t\t\tif i.rs > 1500:\n",
    "\t\t\t\twith open(\"popular_live.txt\",\"a\",encoding=\"utf8\") as f:\n",
    "\t\t\t\t\tf.write(\"{0}--{1}--{2}---{3}--{4}\".format(i.bt,i.xm,i.rs,i.dz,i.fx)+\"\\n\")\n",
    "\n",
    "\n",
    "\tdef __str__(self):\n",
    "\t\treturn \"{0}--{1}--{2}---{3}--{4}\".format(self.bt,self.xm,self.rs,self.dz,self.fx)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "              观看人数         点赞数        评论数       分享数        礼物收入        广告收入  \\\ncount    15.000000   15.000000  12.000000  15.00000   15.000000   15.000000   \nmean   1480.000000  110.666667  28.833333  10.20000  199.333333   45.000000   \nstd     490.189468   40.038672   8.736895   4.61674   90.274713   23.603874   \nmin     800.000000   60.000000  15.000000   4.00000  100.000000   20.000000   \n25%    1150.000000   85.000000  23.750000   7.00000  140.000000   27.500000   \n50%    1400.000000  100.000000  29.000000  10.00000  180.000000   40.000000   \n75%    1750.000000  125.000000  32.750000  11.00000  235.000000   55.000000   \nmax    2500.000000  200.000000  45.000000  20.00000  400.000000  100.000000   \n\n             订阅人数    直播时长（分钟）    观看时长（小时）  \ncount   15.000000   15.000000    15.00000  \nmean    68.000000   87.000000   696.00000  \nstd     27.568098   39.496835   192.42067  \nmin     30.000000   40.000000   420.00000  \n25%     47.500000   60.000000   565.00000  \n50%     60.000000   75.000000   700.00000  \n75%     85.000000  105.000000   775.00000  \nmax    120.000000  180.000000  1050.00000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>15.000000</td>\n      <td>15.000000</td>\n      <td>12.000000</td>\n      <td>15.00000</td>\n      <td>15.000000</td>\n      <td>15.000000</td>\n      <td>15.000000</td>\n      <td>15.000000</td>\n      <td>15.00000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1480.000000</td>\n      <td>110.666667</td>\n      <td>28.833333</td>\n      <td>10.20000</td>\n      <td>199.333333</td>\n      <td>45.000000</td>\n      <td>68.000000</td>\n      <td>87.000000</td>\n      <td>696.00000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>490.189468</td>\n      <td>40.038672</td>\n      <td>8.736895</td>\n      <td>4.61674</td>\n      <td>90.274713</td>\n      <td>23.603874</td>\n      <td>27.568098</td>\n      <td>39.496835</td>\n      <td>192.42067</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>800.000000</td>\n      <td>60.000000</td>\n      <td>15.000000</td>\n      <td>4.00000</td>\n      <td>100.000000</td>\n      <td>20.000000</td>\n      <td>30.000000</td>\n      <td>40.000000</td>\n      <td>420.00000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1150.000000</td>\n      <td>85.000000</td>\n      <td>23.750000</td>\n      <td>7.00000</td>\n      <td>140.000000</td>\n      <td>27.500000</td>\n      <td>47.500000</td>\n      <td>60.000000</td>\n      <td>565.00000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1400.000000</td>\n      <td>100.000000</td>\n      <td>29.000000</td>\n      <td>10.00000</td>\n      <td>180.000000</td>\n      <td>40.000000</td>\n      <td>60.000000</td>\n      <td>75.000000</td>\n      <td>700.00000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1750.000000</td>\n      <td>125.000000</td>\n      <td>32.750000</td>\n      <td>11.00000</td>\n      <td>235.000000</td>\n      <td>55.000000</td>\n      <td>85.000000</td>\n      <td>105.000000</td>\n      <td>775.00000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2500.000000</td>\n      <td>200.000000</td>\n      <td>45.000000</td>\n      <td>20.00000</td>\n      <td>400.000000</td>\n      <td>100.000000</td>\n      <td>120.000000</td>\n      <td>180.000000</td>\n      <td>1050.00000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "696.0"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"观看时长（小时）\"].mean()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "                  时间    直播标题   主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n0    2023/10/1 13:00  美妆技巧分享     小美  1500  120  30.0   10   200    50   100   \n1    2023/10/2 15:30   健身训练课   健康达人  2000  150  40.0   15   300    70    80   \n2    2023/10/3 20:15   美食烹饪秀   厨艺大师  1800  130  35.0   12   250    60    90   \n3    2023/10/4 14:45  音乐直播演奏  音乐家小明  1200   90   NaN   10   150    30    50   \n4    2023/10/5 19:00  时尚搭配分享   时尚达人  1700  110  28.0   10   180    40    70   \n5    2023/10/6 16:30    萌宠乐园   小宠物家   900   80   NaN    5   100    20    30   \n7    2023/10/8 11:45  健康饮食分享    营养师  1400  100  30.0   10   180    40    60   \n8    2023/10/9 18:15  摄影技巧教学  摄影师小李  1600  110  32.0    8   220    50    75   \n9   2023/10/10 14:30    游戏直播   游戏达人  2500  200   NaN   20   400   100   120   \n10  2023/10/11 19:30  艺术绘画展示   画家大师  1100   70  18.0    6   120    25    40   \n11  2023/10/12 17:00  教育知识分享  知识小达人  1300   95  28.0   10   150    30    55   \n13  2023/10/14 12:45  科技科普讲座  科技小天才   800   60  15.0    4   100    20    35   \n14  2023/10/15 20:30  情感心理分享   心理医生  1200   90  25.0   10   160    35    60   \n\n    直播时长（分钟）  观看时长（小时）  \n0         90       750  \n1         60       800  \n2        120       950  \n3         75       600  \n4        100       700  \n5         45       450  \n7         80       550  \n8        110       720  \n9        180      1050  \n10        60       580  \n11        70       650  \n13        40       420  \n14        75       720  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023/10/4 14:45</td>\n      <td>音乐直播演奏</td>\n      <td>音乐家小明</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>NaN</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>75</td>\n      <td>600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023/10/5 19:00</td>\n      <td>时尚搭配分享</td>\n      <td>时尚达人</td>\n      <td>1700</td>\n      <td>110</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>100</td>\n      <td>700</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023/10/6 16:30</td>\n      <td>萌宠乐园</td>\n      <td>小宠物家</td>\n      <td>900</td>\n      <td>80</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>45</td>\n      <td>450</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023/10/8 11:45</td>\n      <td>健康饮食分享</td>\n      <td>营养师</td>\n      <td>1400</td>\n      <td>100</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>80</td>\n      <td>550</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023/10/9 18:15</td>\n      <td>摄影技巧教学</td>\n      <td>摄影师小李</td>\n      <td>1600</td>\n      <td>110</td>\n      <td>32.0</td>\n      <td>8</td>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>110</td>\n      <td>720</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023/10/10 14:30</td>\n      <td>游戏直播</td>\n      <td>游戏达人</td>\n      <td>2500</td>\n      <td>200</td>\n      <td>NaN</td>\n      <td>20</td>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>180</td>\n      <td>1050</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023/10/11 19:30</td>\n      <td>艺术绘画展示</td>\n      <td>画家大师</td>\n      <td>1100</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>6</td>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>60</td>\n      <td>580</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023/10/12 17:00</td>\n      <td>教育知识分享</td>\n      <td>知识小达人</td>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>70</td>\n      <td>650</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023/10/14 12:45</td>\n      <td>科技科普讲座</td>\n      <td>科技小天才</td>\n      <td>800</td>\n      <td>60</td>\n      <td>15.0</td>\n      <td>4</td>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>40</td>\n      <td>420</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023/10/15 20:30</td>\n      <td>情感心理分享</td>\n      <td>心理医生</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>25.0</td>\n      <td>10</td>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>75</td>\n      <td>720</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(subset=[\"主播姓名\"],inplace=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "                  时间     直播标题   主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n0    2023/10/1 13:00   美妆技巧分享     小美  1500  120  30.0   10   200    50   100   \n1    2023/10/2 15:30    健身训练课   健康达人  2000  150  40.0   15   300    70    80   \n2    2023/10/3 20:15    美食烹饪秀   厨艺大师  1800  130  35.0   12   250    60    90   \n3    2023/10/4 14:45   音乐直播演奏  音乐家小明  1200   90   NaN   10   150    30    50   \n4    2023/10/5 19:00   时尚搭配分享   时尚达人  1700  110  28.0   10   180    40    70   \n5    2023/10/6 16:30     萌宠乐园   小宠物家   900   80   NaN    5   100    20    30   \n6    2023/10/7 21:00   旅游美景展示    NaN  2200  180  45.0   18   350    80   110   \n7    2023/10/8 11:45   健康饮食分享    营养师  1400  100  30.0   10   180    40    60   \n8    2023/10/9 18:15   摄影技巧教学  摄影师小李  1600  110  32.0    8   220    50    75   \n9   2023/10/10 14:30     游戏直播   游戏达人  2500  200   NaN   20   400   100   120   \n10  2023/10/11 19:30   艺术绘画展示   画家大师  1100   70  18.0    6   120    25    40   \n11  2023/10/12 17:00   教育知识分享  知识小达人  1300   95  28.0   10   150    30    55   \n12  2023/10/13 22:15  手工DIY课程    NaN  1000   75  20.0    5   130    25    45   \n13  2023/10/14 12:45   科技科普讲座  科技小天才   800   60  15.0    4   100    20    35   \n14  2023/10/15 20:30   情感心理分享   心理医生  1200   90  25.0   10   160    35    60   \n\n    直播时长（分钟）  观看时长（小时） 业务方向  \n0         90       750   美妆  \n1         60       800   健身  \n2        120       950   美食  \n3         75       600   音乐  \n4        100       700   时尚  \n5         45       450   萌宠  \n6        150      1000   旅游  \n7         80       550   健康  \n8        110       720   摄影  \n9        180      1050   游戏  \n10        60       580   艺术  \n11        70       650   教育  \n12        50       500   手工  \n13        40       420   科技  \n14        75       720   情感  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n      <th>业务方向</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n      <td>美妆</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n      <td>健身</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n      <td>美食</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023/10/4 14:45</td>\n      <td>音乐直播演奏</td>\n      <td>音乐家小明</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>NaN</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>75</td>\n      <td>600</td>\n      <td>音乐</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023/10/5 19:00</td>\n      <td>时尚搭配分享</td>\n      <td>时尚达人</td>\n      <td>1700</td>\n      <td>110</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>100</td>\n      <td>700</td>\n      <td>时尚</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023/10/6 16:30</td>\n      <td>萌宠乐园</td>\n      <td>小宠物家</td>\n      <td>900</td>\n      <td>80</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>45</td>\n      <td>450</td>\n      <td>萌宠</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023/10/7 21:00</td>\n      <td>旅游美景展示</td>\n      <td>NaN</td>\n      <td>2200</td>\n      <td>180</td>\n      <td>45.0</td>\n      <td>18</td>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n      <td>150</td>\n      <td>1000</td>\n      <td>旅游</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023/10/8 11:45</td>\n      <td>健康饮食分享</td>\n      <td>营养师</td>\n      <td>1400</td>\n      <td>100</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>80</td>\n      <td>550</td>\n      <td>健康</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023/10/9 18:15</td>\n      <td>摄影技巧教学</td>\n      <td>摄影师小李</td>\n      <td>1600</td>\n      <td>110</td>\n      <td>32.0</td>\n      <td>8</td>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>110</td>\n      <td>720</td>\n      <td>摄影</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023/10/10 14:30</td>\n      <td>游戏直播</td>\n      <td>游戏达人</td>\n      <td>2500</td>\n      <td>200</td>\n      <td>NaN</td>\n      <td>20</td>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>180</td>\n      <td>1050</td>\n      <td>游戏</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023/10/11 19:30</td>\n      <td>艺术绘画展示</td>\n      <td>画家大师</td>\n      <td>1100</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>6</td>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>60</td>\n      <td>580</td>\n      <td>艺术</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023/10/12 17:00</td>\n      <td>教育知识分享</td>\n      <td>知识小达人</td>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>70</td>\n      <td>650</td>\n      <td>教育</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023/10/13 22:15</td>\n      <td>手工DIY课程</td>\n      <td>NaN</td>\n      <td>1000</td>\n      <td>75</td>\n      <td>20.0</td>\n      <td>5</td>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n      <td>50</td>\n      <td>500</td>\n      <td>手工</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023/10/14 12:45</td>\n      <td>科技科普讲座</td>\n      <td>科技小天才</td>\n      <td>800</td>\n      <td>60</td>\n      <td>15.0</td>\n      <td>4</td>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>40</td>\n      <td>420</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023/10/15 20:30</td>\n      <td>情感心理分享</td>\n      <td>心理医生</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>25.0</td>\n      <td>10</td>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>75</td>\n      <td>720</td>\n      <td>情感</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"业务方向\"] = df[\"直播标题\"].apply(lambda x:x[:2])\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "      礼物收入  广告收入  订阅人数\n业务方向                  \n健康     180    40    60\n健身     300    70    80\n情感     160    35    60\n手工     130    25    45\n摄影     220    50    75\n教育     150    30    55\n旅游     350    80   110\n时尚     180    40    70\n游戏     400   100   120\n科技     100    20    35\n美妆     200    50   100\n美食     250    60    90\n艺术     120    25    40\n萌宠     100    20    30\n音乐     150    30    50",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n    </tr>\n    <tr>\n      <th>业务方向</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>健康</th>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>健身</th>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>情感</th>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n    </tr>\n    <tr>\n      <th>手工</th>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>摄影</th>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>教育</th>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n    </tr>\n    <tr>\n      <th>旅游</th>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n    </tr>\n    <tr>\n      <th>时尚</th>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>游戏</th>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>科技</th>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>美妆</th>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>美食</th>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>艺术</th>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>萌宠</th>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>音乐</th>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res1 = df.groupby(\"业务方向\").agg({\"礼物收入\":\"sum\",\"广告收入\":\"sum\"})\n",
    "res2 = df.groupby(\"业务方向\")[[\"订阅人数\"]].sum()\n",
    "res3 = res1.merge(res2,left_index=True,right_index=True)\n",
    "res3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "      礼物收入  广告收入  订阅人数      付费意愿\n业务方向                            \n健康     180    40    60  3.666667\n健身     300    70    80  4.625000\n情感     160    35    60  3.250000\n手工     130    25    45  3.444444\n摄影     220    50    75  3.600000\n教育     150    30    55  3.272727\n旅游     350    80   110  3.909091\n时尚     180    40    70  3.142857\n游戏     400   100   120  4.166667\n科技     100    20    35  3.428571\n美妆     200    50   100  2.500000\n美食     250    60    90  3.444444\n艺术     120    25    40  3.625000\n萌宠     100    20    30  4.000000\n音乐     150    30    50  3.600000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>付费意愿</th>\n    </tr>\n    <tr>\n      <th>业务方向</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>健康</th>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>3.666667</td>\n    </tr>\n    <tr>\n      <th>健身</th>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>4.625000</td>\n    </tr>\n    <tr>\n      <th>情感</th>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>3.250000</td>\n    </tr>\n    <tr>\n      <th>手工</th>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n      <td>3.444444</td>\n    </tr>\n    <tr>\n      <th>摄影</th>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>3.600000</td>\n    </tr>\n    <tr>\n      <th>教育</th>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>3.272727</td>\n    </tr>\n    <tr>\n      <th>旅游</th>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n      <td>3.909091</td>\n    </tr>\n    <tr>\n      <th>时尚</th>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>3.142857</td>\n    </tr>\n    <tr>\n      <th>游戏</th>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>4.166667</td>\n    </tr>\n    <tr>\n      <th>科技</th>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>3.428571</td>\n    </tr>\n    <tr>\n      <th>美妆</th>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>2.500000</td>\n    </tr>\n    <tr>\n      <th>美食</th>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>3.444444</td>\n    </tr>\n    <tr>\n      <th>艺术</th>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>3.625000</td>\n    </tr>\n    <tr>\n      <th>萌宠</th>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>4.000000</td>\n    </tr>\n    <tr>\n      <th>音乐</th>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>3.600000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res3[\"付费意愿\"]= (res3[\"礼物收入\"]+res3[\"广告收入\"])/res3[\"订阅人数\"]\n",
    "res3"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "                时间    直播标题  主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n0  2023/10/1 13:00  美妆技巧分享    小美  1500  120  30.0   10   200    50   100   \n1  2023/10/2 15:30   健身训练课  健康达人  2000  150  40.0   15   300    70    80   \n2  2023/10/3 20:15   美食烹饪秀  厨艺大师  1800  130  35.0   12   250    60    90   \n\n   直播时长（分钟）  观看时长（小时） 业务方向  \n0        90       750   美妆  \n1        60       800   健身  \n2       120       950   美食  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n      <th>业务方向</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n      <td>美妆</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n      <td>健身</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n      <td>美食</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "i =df[\"评论数\"].sum()/ df[\"点赞数\"].sum()\n",
    "k = df[\"分享数\"].sum()/ df[\"评论数\"].sum()\n",
    "k"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "                  时间     直播标题   主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n0    2023/10/1 13:00   美妆技巧分享     小美  1500  120  30.0   10   200    50   100   \n1    2023/10/2 15:30    健身训练课   健康达人  2000  150  40.0   15   300    70    80   \n2    2023/10/3 20:15    美食烹饪秀   厨艺大师  1800  130  35.0   12   250    60    90   \n3    2023/10/4 14:45   音乐直播演奏  音乐家小明  1200   90   NaN   10   150    30    50   \n4    2023/10/5 19:00   时尚搭配分享   时尚达人  1700  110  28.0   10   180    40    70   \n5    2023/10/6 16:30     萌宠乐园   小宠物家   900   80   NaN    5   100    20    30   \n6    2023/10/7 21:00   旅游美景展示    NaN  2200  180  45.0   18   350    80   110   \n7    2023/10/8 11:45   健康饮食分享    营养师  1400  100  30.0   10   180    40    60   \n8    2023/10/9 18:15   摄影技巧教学  摄影师小李  1600  110  32.0    8   220    50    75   \n9   2023/10/10 14:30     游戏直播   游戏达人  2500  200   NaN   20   400   100   120   \n10  2023/10/11 19:30   艺术绘画展示   画家大师  1100   70  18.0    6   120    25    40   \n11  2023/10/12 17:00   教育知识分享  知识小达人  1300   95  28.0   10   150    30    55   \n12  2023/10/13 22:15  手工DIY课程    NaN  1000   75  20.0    5   130    25    45   \n13  2023/10/14 12:45   科技科普讲座  科技小天才   800   60  15.0    4   100    20    35   \n14  2023/10/15 20:30   情感心理分享   心理医生  1200   90  25.0   10   160    35    60   \n\n    直播时长（分钟）  观看时长（小时） 业务方向  \n0         90       750   美妆  \n1         60       800   健身  \n2        120       950   美食  \n3         75       600   音乐  \n4        100       700   时尚  \n5         45       450   萌宠  \n6        150      1000   旅游  \n7         80       550   健康  \n8        110       720   摄影  \n9        180      1050   游戏  \n10        60       580   艺术  \n11        70       650   教育  \n12        50       500   手工  \n13        40       420   科技  \n14        75       720   情感  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n      <th>业务方向</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n      <td>美妆</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n      <td>健身</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n      <td>美食</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023/10/4 14:45</td>\n      <td>音乐直播演奏</td>\n      <td>音乐家小明</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>NaN</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>75</td>\n      <td>600</td>\n      <td>音乐</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023/10/5 19:00</td>\n      <td>时尚搭配分享</td>\n      <td>时尚达人</td>\n      <td>1700</td>\n      <td>110</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>100</td>\n      <td>700</td>\n      <td>时尚</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023/10/6 16:30</td>\n      <td>萌宠乐园</td>\n      <td>小宠物家</td>\n      <td>900</td>\n      <td>80</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>45</td>\n      <td>450</td>\n      <td>萌宠</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023/10/7 21:00</td>\n      <td>旅游美景展示</td>\n      <td>NaN</td>\n      <td>2200</td>\n      <td>180</td>\n      <td>45.0</td>\n      <td>18</td>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n      <td>150</td>\n      <td>1000</td>\n      <td>旅游</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023/10/8 11:45</td>\n      <td>健康饮食分享</td>\n      <td>营养师</td>\n      <td>1400</td>\n      <td>100</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>80</td>\n      <td>550</td>\n      <td>健康</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023/10/9 18:15</td>\n      <td>摄影技巧教学</td>\n      <td>摄影师小李</td>\n      <td>1600</td>\n      <td>110</td>\n      <td>32.0</td>\n      <td>8</td>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>110</td>\n      <td>720</td>\n      <td>摄影</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023/10/10 14:30</td>\n      <td>游戏直播</td>\n      <td>游戏达人</td>\n      <td>2500</td>\n      <td>200</td>\n      <td>NaN</td>\n      <td>20</td>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>180</td>\n      <td>1050</td>\n      <td>游戏</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023/10/11 19:30</td>\n      <td>艺术绘画展示</td>\n      <td>画家大师</td>\n      <td>1100</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>6</td>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>60</td>\n      <td>580</td>\n      <td>艺术</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023/10/12 17:00</td>\n      <td>教育知识分享</td>\n      <td>知识小达人</td>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>70</td>\n      <td>650</td>\n      <td>教育</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023/10/13 22:15</td>\n      <td>手工DIY课程</td>\n      <td>NaN</td>\n      <td>1000</td>\n      <td>75</td>\n      <td>20.0</td>\n      <td>5</td>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n      <td>50</td>\n      <td>500</td>\n      <td>手工</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023/10/14 12:45</td>\n      <td>科技科普讲座</td>\n      <td>科技小天才</td>\n      <td>800</td>\n      <td>60</td>\n      <td>15.0</td>\n      <td>4</td>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>40</td>\n      <td>420</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023/10/15 20:30</td>\n      <td>情感心理分享</td>\n      <td>心理医生</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>25.0</td>\n      <td>10</td>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>75</td>\n      <td>720</td>\n      <td>情感</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"评论数\"].fillna(df[\"评论数\"].median())\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "0     10\n1     15\n2     12\n3     10\n4     10\n5      5\n6     18\n7     10\n8      8\n9     20\n10     6\n11    10\n12     5\n13     4\n14    10\nName: 分享数, dtype: int64"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"分享数\"].fillna(df[\"分享数\"].mode())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "outputs": [
    {
     "data": {
      "text/plain": "[<__main__.LiveShow at 0x298911d5640>,\n <__main__.LiveShow at 0x298911d58b0>,\n <__main__.LiveShow at 0x29891194dc0>,\n <__main__.LiveShow at 0x298911940a0>,\n <__main__.LiveShow at 0x29891194460>,\n <__main__.LiveShow at 0x29891194490>,\n <__main__.LiveShow at 0x29891194550>,\n <__main__.LiveShow at 0x29891194b80>,\n <__main__.LiveShow at 0x29891194d00>,\n <__main__.LiveShow at 0x298911946a0>,\n <__main__.LiveShow at 0x29891194f10>,\n <__main__.LiveShow at 0x29891194190>,\n <__main__.LiveShow at 0x29891194070>,\n <__main__.LiveShow at 0x29891194940>,\n <__main__.LiveShow at 0x29891194220>]"
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = []\n",
    "for i in df.index:\n",
    "\tzt = df.loc[i,\"直播标题\"]\n",
    "\txm = df.loc[i,\"主播姓名\"]\n",
    "\trs = df.loc[i,\"观看人数\"]\n",
    "\tdz = df.loc[i,\"点赞数\"]\n",
    "\tpl = df.loc[i,\"评论数\"]\n",
    "\tfx = df.loc[i,\"业务方向\"]\n",
    "\tls = LiveShow(zt,xm,rs,dz,pl,fx)\n",
    "\tl.append(ls)\n",
    "l"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "outputs": [
    {
     "data": {
      "text/plain": "0.07477477477477477"
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lv = LiveShow()\n",
    "lv.get_rate(l)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "游戏直播--游戏达人--2500---200--游戏\n"
     ]
    }
   ],
   "source": [
    "print(lv.cal_person_count(l))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [
    {
     "data": {
      "text/plain": "{'健康',\n '健身',\n '情感',\n '手工',\n '摄影',\n '教育',\n '旅游',\n '时尚',\n '游戏',\n '科技',\n '美妆',\n '美食',\n '艺术',\n '萌宠',\n '音乐'}"
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lv.stat_biz_type()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "健身训练课--健康达人--2000---150--健身\n",
      "美食烹饪秀--厨艺大师--1800---130--美食\n",
      "时尚搭配分享--时尚达人--1700---110--时尚\n",
      "旅游美景展示--nan--2200---180--旅游\n",
      "摄影技巧教学--摄影师小李--1600---110--摄影\n",
      "游戏直播--游戏达人--2500---200--游戏\n"
     ]
    }
   ],
   "source": [
    "for i in lv.get_hot(l):\n",
    "\tprint(i)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'LiveShow' object has no attribute 'zt'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mAttributeError\u001B[0m                            Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[65], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mlv\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msave\u001B[49m\u001B[43m(\u001B[49m\u001B[43ml\u001B[49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[59], line 41\u001B[0m, in \u001B[0;36mLiveShow.save\u001B[1;34m(self, l)\u001B[0m\n\u001B[0;32m     39\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m i\u001B[38;5;241m.\u001B[39mrs \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1500\u001B[39m:\n\u001B[0;32m     40\u001B[0m \t\u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mopen\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpopular_live.txt\u001B[39m\u001B[38;5;124m\"\u001B[39m,\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124ma\u001B[39m\u001B[38;5;124m\"\u001B[39m,encoding\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mutf8\u001B[39m\u001B[38;5;124m\"\u001B[39m) \u001B[38;5;28;01mas\u001B[39;00m f:\n\u001B[1;32m---> 41\u001B[0m \t\tf\u001B[38;5;241m.\u001B[39mwrite(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{0}\u001B[39;00m\u001B[38;5;124m--\u001B[39m\u001B[38;5;132;01m{1}\u001B[39;00m\u001B[38;5;124m--\u001B[39m\u001B[38;5;132;01m{2}\u001B[39;00m\u001B[38;5;124m---\u001B[39m\u001B[38;5;132;01m{3}\u001B[39;00m\u001B[38;5;124m--\u001B[39m\u001B[38;5;132;01m{4}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mformat(\u001B[43mi\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mzt\u001B[49m,i\u001B[38;5;241m.\u001B[39mxm,i\u001B[38;5;241m.\u001B[39mrs,i\u001B[38;5;241m.\u001B[39mdz,i\u001B[38;5;241m.\u001B[39mfx)\u001B[38;5;241m+\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124m\"\u001B[39m)\n",
      "\u001B[1;31mAttributeError\u001B[0m: 'LiveShow' object has no attribute 'zt'"
     ]
    }
   ],
   "source": [
    "lv.save(l)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "2990"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"礼物收入\"].sum()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "data": {
      "text/plain": "                  时间     直播标题   主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n13  2023/10/14 12:45   科技科普讲座  科技小天才   800   60  15.0    4   100    20    35   \n5    2023/10/6 16:30     萌宠乐园   小宠物家   900   80   NaN    5   100    20    30   \n12  2023/10/13 22:15  手工DIY课程    NaN  1000   75  20.0    5   130    25    45   \n10  2023/10/11 19:30   艺术绘画展示   画家大师  1100   70  18.0    6   120    25    40   \n3    2023/10/4 14:45   音乐直播演奏  音乐家小明  1200   90   NaN   10   150    30    50   \n14  2023/10/15 20:30   情感心理分享   心理医生  1200   90  25.0   10   160    35    60   \n11  2023/10/12 17:00   教育知识分享  知识小达人  1300   95  28.0   10   150    30    55   \n7    2023/10/8 11:45   健康饮食分享    营养师  1400  100  30.0   10   180    40    60   \n0    2023/10/1 13:00   美妆技巧分享     小美  1500  120  30.0   10   200    50   100   \n8    2023/10/9 18:15   摄影技巧教学  摄影师小李  1600  110  32.0    8   220    50    75   \n4    2023/10/5 19:00   时尚搭配分享   时尚达人  1700  110  28.0   10   180    40    70   \n2    2023/10/3 20:15    美食烹饪秀   厨艺大师  1800  130  35.0   12   250    60    90   \n1    2023/10/2 15:30    健身训练课   健康达人  2000  150  40.0   15   300    70    80   \n6    2023/10/7 21:00   旅游美景展示    NaN  2200  180  45.0   18   350    80   110   \n9   2023/10/10 14:30     游戏直播   游戏达人  2500  200   NaN   20   400   100   120   \n\n    直播时长（分钟）  观看时长（小时） 业务方向  \n13        40       420   科技  \n5         45       450   萌宠  \n12        50       500   手工  \n10        60       580   艺术  \n3         75       600   音乐  \n14        75       720   情感  \n11        70       650   教育  \n7         80       550   健康  \n0         90       750   美妆  \n8        110       720   摄影  \n4        100       700   时尚  \n2        120       950   美食  \n1         60       800   健身  \n6        150      1000   旅游  \n9        180      1050   游戏  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n      <th>业务方向</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13</th>\n      <td>2023/10/14 12:45</td>\n      <td>科技科普讲座</td>\n      <td>科技小天才</td>\n      <td>800</td>\n      <td>60</td>\n      <td>15.0</td>\n      <td>4</td>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>40</td>\n      <td>420</td>\n      <td>科技</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023/10/6 16:30</td>\n      <td>萌宠乐园</td>\n      <td>小宠物家</td>\n      <td>900</td>\n      <td>80</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>45</td>\n      <td>450</td>\n      <td>萌宠</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023/10/13 22:15</td>\n      <td>手工DIY课程</td>\n      <td>NaN</td>\n      <td>1000</td>\n      <td>75</td>\n      <td>20.0</td>\n      <td>5</td>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n      <td>50</td>\n      <td>500</td>\n      <td>手工</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023/10/11 19:30</td>\n      <td>艺术绘画展示</td>\n      <td>画家大师</td>\n      <td>1100</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>6</td>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>60</td>\n      <td>580</td>\n      <td>艺术</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023/10/4 14:45</td>\n      <td>音乐直播演奏</td>\n      <td>音乐家小明</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>NaN</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>75</td>\n      <td>600</td>\n      <td>音乐</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023/10/15 20:30</td>\n      <td>情感心理分享</td>\n      <td>心理医生</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>25.0</td>\n      <td>10</td>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>75</td>\n      <td>720</td>\n      <td>情感</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023/10/12 17:00</td>\n      <td>教育知识分享</td>\n      <td>知识小达人</td>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>70</td>\n      <td>650</td>\n      <td>教育</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023/10/8 11:45</td>\n      <td>健康饮食分享</td>\n      <td>营养师</td>\n      <td>1400</td>\n      <td>100</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>80</td>\n      <td>550</td>\n      <td>健康</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n      <td>美妆</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023/10/9 18:15</td>\n      <td>摄影技巧教学</td>\n      <td>摄影师小李</td>\n      <td>1600</td>\n      <td>110</td>\n      <td>32.0</td>\n      <td>8</td>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>110</td>\n      <td>720</td>\n      <td>摄影</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023/10/5 19:00</td>\n      <td>时尚搭配分享</td>\n      <td>时尚达人</td>\n      <td>1700</td>\n      <td>110</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>100</td>\n      <td>700</td>\n      <td>时尚</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n      <td>美食</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n      <td>健身</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023/10/7 21:00</td>\n      <td>旅游美景展示</td>\n      <td>NaN</td>\n      <td>2200</td>\n      <td>180</td>\n      <td>45.0</td>\n      <td>18</td>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n      <td>150</td>\n      <td>1000</td>\n      <td>旅游</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023/10/10 14:30</td>\n      <td>游戏直播</td>\n      <td>游戏达人</td>\n      <td>2500</td>\n      <td>200</td>\n      <td>NaN</td>\n      <td>20</td>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>180</td>\n      <td>1050</td>\n      <td>游戏</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"观看人数\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "outputs": [
    {
     "data": {
      "text/plain": "                  时间     直播标题   主播姓名  观看人数  点赞数   评论数  分享数  礼物收入  广告收入  订阅人数  \\\n0    2023/10/1 13:00   美妆技巧分享     小美  1500  120  30.0   10   200    50   100   \n1    2023/10/2 15:30    健身训练课   健康达人  2000  150  40.0   15   300    70    80   \n2    2023/10/3 20:15    美食烹饪秀   厨艺大师  1800  130  35.0   12   250    60    90   \n3    2023/10/4 14:45   音乐直播演奏  音乐家小明  1200   90   NaN   10   150    30    50   \n4    2023/10/5 19:00   时尚搭配分享   时尚达人  1700  110  28.0   10   180    40    70   \n5    2023/10/6 16:30     萌宠乐园   小宠物家   900   80   NaN    5   100    20    30   \n6    2023/10/7 21:00   旅游美景展示    NaN  2200  180  45.0   18   350    80   110   \n7    2023/10/8 11:45   健康饮食分享    营养师  1400  100  30.0   10   180    40    60   \n8    2023/10/9 18:15   摄影技巧教学  摄影师小李  1600  110  32.0    8   220    50    75   \n9   2023/10/10 14:30     游戏直播   游戏达人  2500  200   NaN   20   400   100   120   \n10  2023/10/11 19:30   艺术绘画展示   画家大师  1100   70  18.0    6   120    25    40   \n11  2023/10/12 17:00   教育知识分享  知识小达人  1300   95  28.0   10   150    30    55   \n12  2023/10/13 22:15  手工DIY课程    NaN  1000   75  20.0    5   130    25    45   \n13  2023/10/14 12:45   科技科普讲座  科技小天才   800   60  15.0    4   100    20    35   \n14  2023/10/15 20:30   情感心理分享   心理医生  1200   90  25.0   10   160    35    60   \n\n    直播时长（分钟）  观看时长（小时） 业务方向  总收入  \n0         90       750   美妆  250  \n1         60       800   健身  370  \n2        120       950   美食  310  \n3         75       600   音乐  180  \n4        100       700   时尚  220  \n5         45       450   萌宠  120  \n6        150      1000   旅游  430  \n7         80       550   健康  220  \n8        110       720   摄影  270  \n9        180      1050   游戏  500  \n10        60       580   艺术  145  \n11        70       650   教育  180  \n12        50       500   手工  155  \n13        40       420   科技  120  \n14        75       720   情感  195  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>时间</th>\n      <th>直播标题</th>\n      <th>主播姓名</th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>分享数</th>\n      <th>礼物收入</th>\n      <th>广告收入</th>\n      <th>订阅人数</th>\n      <th>直播时长（分钟）</th>\n      <th>观看时长（小时）</th>\n      <th>业务方向</th>\n      <th>总收入</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023/10/1 13:00</td>\n      <td>美妆技巧分享</td>\n      <td>小美</td>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>200</td>\n      <td>50</td>\n      <td>100</td>\n      <td>90</td>\n      <td>750</td>\n      <td>美妆</td>\n      <td>250</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023/10/2 15:30</td>\n      <td>健身训练课</td>\n      <td>健康达人</td>\n      <td>2000</td>\n      <td>150</td>\n      <td>40.0</td>\n      <td>15</td>\n      <td>300</td>\n      <td>70</td>\n      <td>80</td>\n      <td>60</td>\n      <td>800</td>\n      <td>健身</td>\n      <td>370</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023/10/3 20:15</td>\n      <td>美食烹饪秀</td>\n      <td>厨艺大师</td>\n      <td>1800</td>\n      <td>130</td>\n      <td>35.0</td>\n      <td>12</td>\n      <td>250</td>\n      <td>60</td>\n      <td>90</td>\n      <td>120</td>\n      <td>950</td>\n      <td>美食</td>\n      <td>310</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023/10/4 14:45</td>\n      <td>音乐直播演奏</td>\n      <td>音乐家小明</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>NaN</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>50</td>\n      <td>75</td>\n      <td>600</td>\n      <td>音乐</td>\n      <td>180</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023/10/5 19:00</td>\n      <td>时尚搭配分享</td>\n      <td>时尚达人</td>\n      <td>1700</td>\n      <td>110</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>70</td>\n      <td>100</td>\n      <td>700</td>\n      <td>时尚</td>\n      <td>220</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2023/10/6 16:30</td>\n      <td>萌宠乐园</td>\n      <td>小宠物家</td>\n      <td>900</td>\n      <td>80</td>\n      <td>NaN</td>\n      <td>5</td>\n      <td>100</td>\n      <td>20</td>\n      <td>30</td>\n      <td>45</td>\n      <td>450</td>\n      <td>萌宠</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2023/10/7 21:00</td>\n      <td>旅游美景展示</td>\n      <td>NaN</td>\n      <td>2200</td>\n      <td>180</td>\n      <td>45.0</td>\n      <td>18</td>\n      <td>350</td>\n      <td>80</td>\n      <td>110</td>\n      <td>150</td>\n      <td>1000</td>\n      <td>旅游</td>\n      <td>430</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2023/10/8 11:45</td>\n      <td>健康饮食分享</td>\n      <td>营养师</td>\n      <td>1400</td>\n      <td>100</td>\n      <td>30.0</td>\n      <td>10</td>\n      <td>180</td>\n      <td>40</td>\n      <td>60</td>\n      <td>80</td>\n      <td>550</td>\n      <td>健康</td>\n      <td>220</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2023/10/9 18:15</td>\n      <td>摄影技巧教学</td>\n      <td>摄影师小李</td>\n      <td>1600</td>\n      <td>110</td>\n      <td>32.0</td>\n      <td>8</td>\n      <td>220</td>\n      <td>50</td>\n      <td>75</td>\n      <td>110</td>\n      <td>720</td>\n      <td>摄影</td>\n      <td>270</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2023/10/10 14:30</td>\n      <td>游戏直播</td>\n      <td>游戏达人</td>\n      <td>2500</td>\n      <td>200</td>\n      <td>NaN</td>\n      <td>20</td>\n      <td>400</td>\n      <td>100</td>\n      <td>120</td>\n      <td>180</td>\n      <td>1050</td>\n      <td>游戏</td>\n      <td>500</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2023/10/11 19:30</td>\n      <td>艺术绘画展示</td>\n      <td>画家大师</td>\n      <td>1100</td>\n      <td>70</td>\n      <td>18.0</td>\n      <td>6</td>\n      <td>120</td>\n      <td>25</td>\n      <td>40</td>\n      <td>60</td>\n      <td>580</td>\n      <td>艺术</td>\n      <td>145</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2023/10/12 17:00</td>\n      <td>教育知识分享</td>\n      <td>知识小达人</td>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>10</td>\n      <td>150</td>\n      <td>30</td>\n      <td>55</td>\n      <td>70</td>\n      <td>650</td>\n      <td>教育</td>\n      <td>180</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2023/10/13 22:15</td>\n      <td>手工DIY课程</td>\n      <td>NaN</td>\n      <td>1000</td>\n      <td>75</td>\n      <td>20.0</td>\n      <td>5</td>\n      <td>130</td>\n      <td>25</td>\n      <td>45</td>\n      <td>50</td>\n      <td>500</td>\n      <td>手工</td>\n      <td>155</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2023/10/14 12:45</td>\n      <td>科技科普讲座</td>\n      <td>科技小天才</td>\n      <td>800</td>\n      <td>60</td>\n      <td>15.0</td>\n      <td>4</td>\n      <td>100</td>\n      <td>20</td>\n      <td>35</td>\n      <td>40</td>\n      <td>420</td>\n      <td>科技</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2023/10/15 20:30</td>\n      <td>情感心理分享</td>\n      <td>心理医生</td>\n      <td>1200</td>\n      <td>90</td>\n      <td>25.0</td>\n      <td>10</td>\n      <td>160</td>\n      <td>35</td>\n      <td>60</td>\n      <td>75</td>\n      <td>720</td>\n      <td>情感</td>\n      <td>195</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"总收入\"] = df[\"礼物收入\"]+df[\"广告收入\"]\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x29895a504c0>]"
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(rotation='45')\n",
    "plt.plot(df[\"时间\"],df[\"观看人数\"])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 13 artists>"
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = df.groupby(\"主播姓名\")[\"点赞数\"].mean()\n",
    "plt.bar(res.index,res.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "([<matplotlib.patches.Wedge at 0x29895a2fb20>,\n  <matplotlib.patches.Wedge at 0x29895a4bf10>],\n [Text(-0.9209510303404338, 0.6015390259284042, '礼物收入'),\n  Text(0.9209510866605782, -0.6015389397027431, '广告收入')],\n [Text(-0.5023369256402366, 0.3281121959609477, '81.6%'),\n  Text(0.5023369563603153, -0.3281121489287689, '18.4%')])"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data5 = np.array([df[\"礼物收入\"].sum(),df[\"广告收入\"].sum()])\n",
    "data5\n",
    "plt.pie(data5,labels=[\"礼物收入\",\"广告收入\"],autopct='%.1f%%')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x29895caf640>"
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"观看人数\"],df[\"点赞数\"])\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "outputs": [
    {
     "data": {
      "text/plain": "{'whiskers': [<matplotlib.lines.Line2D at 0x29895670220>,\n  <matplotlib.lines.Line2D at 0x2989550a2e0>],\n 'caps': [<matplotlib.lines.Line2D at 0x29895065ac0>,\n  <matplotlib.lines.Line2D at 0x29895b8f880>],\n 'boxes': [<matplotlib.lines.Line2D at 0x29895ca76a0>],\n 'medians': [<matplotlib.lines.Line2D at 0x29895b8fd00>],\n 'fliers': [<matplotlib.lines.Line2D at 0x29895b8f6d0>],\n 'means': []}"
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2 = df.groupby(\"业务方向\")[\"观看时长（小时）\"].sum()\n",
    "plt.boxplot(res2)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "15"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sqlalchemy import create_engine\n",
    "conn = create_engine('mysql+pymysql://root:123456@localhost/db_16')\n",
    "df.to_sql(\"live\",conn)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [],
   "source": [
    "import pymysql\n",
    "connect = pymysql.connect(host=\"localhost\",port=3306,user=\"root\",password=\"123456\",db=\"db_16\")\n",
    "\n",
    "cursor = connect.cursor()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "outputs": [
    {
     "data": {
      "text/plain": "((0,\n  '2023/10/1 13:00',\n  '美妆技巧分享',\n  '小美',\n  1500,\n  120,\n  30.0,\n  10,\n  200,\n  50,\n  100,\n  90,\n  750,\n  '美妆',\n  250),\n (1,\n  '2023/10/2 15:30',\n  '健身训练课',\n  '健康达人',\n  2000,\n  150,\n  40.0,\n  15,\n  300,\n  70,\n  80,\n  60,\n  800,\n  '健身',\n  370),\n (2,\n  '2023/10/3 20:15',\n  '美食烹饪秀',\n  '厨艺大师',\n  1800,\n  130,\n  35.0,\n  12,\n  250,\n  60,\n  90,\n  120,\n  950,\n  '美食',\n  310),\n (3,\n  '2023/10/4 14:45',\n  '音乐直播演奏',\n  '音乐家小明',\n  1200,\n  90,\n  None,\n  10,\n  150,\n  30,\n  50,\n  75,\n  600,\n  '音乐',\n  180),\n (4,\n  '2023/10/5 19:00',\n  '时尚搭配分享',\n  '时尚达人',\n  1700,\n  110,\n  28.0,\n  10,\n  180,\n  40,\n  70,\n  100,\n  700,\n  '时尚',\n  220),\n (5,\n  '2023/10/6 16:30',\n  '萌宠乐园',\n  '小宠物家',\n  900,\n  80,\n  None,\n  5,\n  100,\n  20,\n  30,\n  45,\n  450,\n  '萌宠',\n  120),\n (6,\n  '2023/10/7 21:00',\n  '旅游美景展示',\n  None,\n  2200,\n  180,\n  45.0,\n  18,\n  350,\n  80,\n  110,\n  150,\n  1000,\n  '旅游',\n  430),\n (7,\n  '2023/10/8 11:45',\n  '健康饮食分享',\n  '营养师',\n  1400,\n  100,\n  30.0,\n  10,\n  180,\n  40,\n  60,\n  80,\n  550,\n  '健康',\n  220),\n (8,\n  '2023/10/9 18:15',\n  '摄影技巧教学',\n  '摄影师小李',\n  1600,\n  110,\n  32.0,\n  8,\n  220,\n  50,\n  75,\n  110,\n  720,\n  '摄影',\n  270),\n (9,\n  '2023/10/10 14:30',\n  '游戏直播',\n  '游戏达人',\n  2500,\n  200,\n  None,\n  20,\n  400,\n  100,\n  120,\n  180,\n  1050,\n  '游戏',\n  500),\n (10,\n  '2023/10/11 19:30',\n  '艺术绘画展示',\n  '画家大师',\n  1100,\n  70,\n  18.0,\n  6,\n  120,\n  25,\n  40,\n  60,\n  580,\n  '艺术',\n  145),\n (11,\n  '2023/10/12 17:00',\n  '教育知识分享',\n  '知识小达人',\n  1300,\n  95,\n  28.0,\n  10,\n  150,\n  30,\n  55,\n  70,\n  650,\n  '教育',\n  180),\n (12,\n  '2023/10/13 22:15',\n  '手工DIY课程',\n  None,\n  1000,\n  75,\n  20.0,\n  5,\n  130,\n  25,\n  45,\n  50,\n  500,\n  '手工',\n  155),\n (13,\n  '2023/10/14 12:45',\n  '科技科普讲座',\n  '科技小天才',\n  800,\n  60,\n  15.0,\n  4,\n  100,\n  20,\n  35,\n  40,\n  420,\n  '科技',\n  120),\n (14,\n  '2023/10/15 20:30',\n  '情感心理分享',\n  '心理医生',\n  1200,\n  90,\n  25.0,\n  10,\n  160,\n  35,\n  60,\n  75,\n  720,\n  '情感',\n  195))"
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql  = \"select * from live\"\n",
    "cursor.execute(sql)\n",
    "cursor.fetchall()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "outputs": [],
   "source": [
    "sql = \"update live set 业务方向= '热门' where 观看人数 > %s\"\n",
    "cursor.execute(sql,args=(1800,))\n",
    "connect.commit()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [],
   "source": [
    "sql = \"delete from live where 评论数 < %s\"\n",
    "cursor.execute(sql,args=(30,))\n",
    "connect.commit()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "((0,\n  '2023/10/1 13:00',\n  '美妆技巧分享',\n  '小美',\n  1500,\n  120,\n  30.0,\n  10,\n  200,\n  50,\n  100,\n  90,\n  750,\n  '美妆',\n  250),\n (2,\n  '2023/10/3 20:15',\n  '美食烹饪秀',\n  '厨艺大师',\n  1800,\n  130,\n  35.0,\n  12,\n  250,\n  60,\n  90,\n  120,\n  950,\n  '美食',\n  310),\n (6,\n  '2023/10/7 21:00',\n  '旅游美景展示',\n  None,\n  2200,\n  180,\n  45.0,\n  18,\n  350,\n  80,\n  110,\n  150,\n  1000,\n  '热门',\n  430))"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sql = \"select * from live where 直播标题 like '%%美%%' \"\n",
    "cursor.execute(sql)\n",
    "cursor.fetchall()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_10640\\3861252571.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X.fillna(0,inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "X = df[[\"观看人数\",\"点赞数\",\"评论数\"]]\n",
    "y = df[\"礼物收入\"]\n",
    "X.fillna(0,inplace=True)\n",
    "y.fillna(0,inplace=True)\n",
    "X.corr()\n",
    "sns.heatmap(X, annot=True, cmap='YlGnBu', fmt='.1f')\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=42)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.74256522e-03  1.89250382e+00  1.11842118e+00]\n",
      "-34.068599800341815\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge\n",
    "clf = LinearRegression().fit(X_train,y_train)\n",
    "# 相关系数\n",
    "print(clf.coef_)\n",
    "# 截距\n",
    "print(clf.intercept_)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.50554855e-03  1.89014590e+00  1.11616520e+00]\n",
      "-34.10362120015671\n"
     ]
    }
   ],
   "source": [
    "clf1 = Ridge().fit(X_train,y_train)\n",
    "# 相关系数\n",
    "print(clf1.coef_)\n",
    "# 截距\n",
    "print(clf1.intercept_)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "y_pred1 = clf1.predict(X_test)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "data": {
      "text/plain": "1593.0155446693645"
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_pred)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "data": {
      "text/plain": "1588.8714700464327"
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(y_test,y_pred1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "data": {
      "text/plain": "   观看人数  点赞数   评论数  礼物收入        pred\n0  2500  200   0.0   400  340.161688\n1  1300   95  28.0   150  174.755652\n2  1500  120  30.0   200  223.940520",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>观看人数</th>\n      <th>点赞数</th>\n      <th>评论数</th>\n      <th>礼物收入</th>\n      <th>pred</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2500</td>\n      <td>200</td>\n      <td>0.0</td>\n      <td>400</td>\n      <td>340.161688</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1300</td>\n      <td>95</td>\n      <td>28.0</td>\n      <td>150</td>\n      <td>174.755652</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1500</td>\n      <td>120</td>\n      <td>30.0</td>\n      <td>200</td>\n      <td>223.940520</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.concat([X_test,y_test],axis=1).reset_index(drop=True)\n",
    "result = pd.concat([result,pd.DataFrame(y_pred1,columns=['pred'])],axis=1)\n",
    "result\n",
    "# result.to_sql(\"tbl_live\",con=conn)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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    "name": "ipython",
    "version": 2
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